Video coding is a fundamental and key task in video streaming and communication systems. For example, video coding provides an important role in relieving the burden with respect to storage and transmission in various practical multimedia processing and communication systems.
High Efficiency Video Coding (HEVC) has become the most popular video coding standard after its final standardization release in 2013 by the Joint Collaborative Team on Video Coding (JCTVC) of the ITU-T Video Coding Experts Group (VCEG) and the ISO Moving Picture Experts Group (MPEG). HEVC doubles the coding efficiency as compared to its predecessor H.264/AVC standard by adopting new and complex coding tools.
Rate control (RC) is an important technique to improve the video coding performance with respect to storage and transmission. RC is widely used in video coding and transmission systems, such as systems which encode the Group of Pictures (GOPs), frames, and Coding Tree Units (CTUs) under a bit rate constraint with the goal of achieving better RC performances by setting Quantization parameters (QPs) properly. Accordingly, bit allocation at different coding levels (e.g., group of pictures (GOP), frame, and coding tree unit (CTU)) is a key step of rate control to improve the coding performance. Current bit allocation schemes are typically based on Rate-Distortion (R-D) models because of the higher optimization performance. In general, the objectives of RC optimization typically include improving R-D performance, achieving accurate bit rate achievements, maintaining coding quality smoothness, and stable buffer control to avoid the occurrences of overflow and underflow cases. Therefore, the modeling accuracy of R-D relationships for CTUs is important in many video coding schemes, and is often a prerequisite for the overall coding efficiency gains.
Prior efforts to optimize the RC performance have included efforts to optimize RC performance for different coding structures (e.g., all intra (AI), low delay B and P (LB/LP), and random access (RA)), for different optimization objectives (e.g., R-D performance, quality smoothness, and buffer control), and at different coding levels (e.g., GOP level, frame level, and block level). There have essentially been three categories for prior RC optimization methods. One such category of RC optimization methods includes the Q-domain based RC methods which achieve better R-D performances by exploring the relationships between Quantization step (Qstep), the coding distortion D, and the consumed bits R. The typical work for HEVC is the pixel-wise Unified Rate Quantization (URQ) model. Another category of RC optimization methods includes the λ-domain RC based methods which jointly consider the Lagrange multiplier decisions for both Rate-Distortion-Optimization (RDO) and RC. The typical work for HEVC is the R-λ model based RC with QP-λ relationship, which has been adopted by JCTVC and implemented in the latest HM16.8. The final such category of RC optimization methods includes the ρ-domain based RC methods which reveal the relationships between the percentage of zero quantized Discrete Cosine Transform (DCT) coefficients and R, D, Qstep, respectively, for the further RC optimization.
In the current existing video transmission and communication market, other than some video coding products which adopt the H.264 coding standard, the HEVC based video encoding and decoding systems adopt the R-λ model based RC method. However, the R-λ model based RC methods do not handle CTU level bit allocation optimization well because of the drastic motions and scene changes for CTUs among adjacent frames. For example, the aforementioned RC methods adopt spatial-temporal prediction and regression methods for the prediction and updating of R-D model parameters. However, such spatial-temporal prediction and regression methods are not always accurate with respect to the prediction and updating of R-D model parameters, especially for CTUs.